Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

InsurTalks Podcast with Horacio Sanchez-Granel: Can Insurers respond swiftly to customer needs during a Pandemic?

7 minutes, 50 seconds read

According to the International Monetary Fund (IMF), the global economy is expected to shrink by over 3 percent in 2020 – the steepest slowdown since the Great Depression of the 1930s. To understand the impact of the COVID crisis in the Latin American Insurance Industry, we interviewed Mr. Horacio Sanchez- Granel from Buenos Aires, Argentina, and Insurance & Reinsurance Consultant.

Mr. Horacio has been Chairman and CEO of Boston Seguros, P&C and Life Insurance company for 21 years. Previously, he held senior executive positions in three insurance companies and has several years of experience managing financial service institutions. He has also held other executive positions in an Argentinian oil company and a tractor and industrial machinery international company. Currently, he is a Board Member and part of the Executive Committee in Nacion Seguros, the state-owned insurance company. He also works as an insurance and reinsurance consultant for Argentine and Latin American markets.

Connect with Mr. Horacio – LinkedIn

The excerpt from the interview:

Customer Relationship during the pandemic

Insurance companies play a pivotal role during times of economic stress by helping companies and individuals manage risks and cushion against losses. How should Insurers respond to their customer’s needs, especially since there will be scrutiny about how they respond during this critical time — and it will dictate public perception for many years to come?

Mr. Horacio: Insurers must safeguard the interests of their clients and advise them on the scope of the coverage. They must communicate that the coverage is not infinite but rather has limits in terms of the risks covered, amount insured, and the origin of the claim. There are doubtful cases but insurers should be flexible enough and protect their client from damages. It was not possible to predict COVID-19. Both Life and P&Cs have been affected and have huge arrears to be paid. 

On the other hand, claims processes need to be more transparent. They should adhere to the compliances of the insurance companies. In these times, selling agents and insurance brokers should be more flexible and build close relationships with the clients. They should explain to them the possibility of the claim they are trying to reimburse. In Latin America, we don’t have many claims related to business interruption. That coverage is not very common here. 

Especially in Argentina, businesses have slowed down due to lockdowns. The claims ratio in this area is going down but claims in life insurance policies have increased a bit. However, the impact here is not as big as the USA or Europe.  

Business Continuity in the time of Pandemics

What are some new business models that Insurance Carriers are considering to meet the expectations of life in ‘The New Normal’? More specifically, where is the new business going to come from, for Insurance, over the next two years?

Mr. Horacio: The outbreak of the COVID-19 pandemic has changed the dynamics of work culture, human relationships, and daily routines. Many companies including insurers are adopting digital solutions within their operations. Organizations are reimagining their business models to adapt to new paradigms to be more sustainable and profitable. 

The New Normal has given rise to new coverages in various insurance lines to cover risks originated by this pandemic or any possible future pandemics. 

For example, new clauses such as Loss of Profit due to business interruption and pandemics in Life and Health insurance, and worker’s compensation will now be included in the respective policies. Interruption of business processes entails new set-up and investment. Some other new coverages will also be introduced such as the cost of maintenance due to the non-use of offices, premises, or industrial facilities. Cyber Insurance will now be a must as most of the workforce is working remotely. The rate of cybercrimes was much higher in developed countries before the pandemic, but now even the developing countries are at risk. This will accelerate the need for Cyber Insurance in developing countries. 

Road to recovery

Many General Insurance lines are hit- Travel, Motor, Home – what will be the road to recovery for these Insurance lines?

Mr. Horacio: Since people are avoiding travel altogether, the travel and motor insurance industry are hit badly. Many customers in Argentina are asking Insurance companies to give some discount on the premiums. We will see some big changes in these insurance lines. Going forward, on-demand or pay-as-you-use policies will prevail more in these insurance lines. 

Role of AI in pandemic crisis management

Before the Pandemic crisis began, technologies like AI have been instrumental in modernizing the business of insurance and advancing their digital transformation. Where are some of the biggest gaps being exposed to insurance organizations, and How is technology going to solve these problems?

Mr. Horacio: Going forward, AI along with IoT and other technologies will play a crucial role in the Insurance industry as a whole. They will rely on statistical analysis of large databases to predict future behaviors. The new challenge is how to incorporate unknown risks into the existing models to be able to properly underwrite and price risks, anticipate client behavior, facilitate complex operating processes, manage complex claims and detect possible frauds.

Financial assets are the main asset of an insurance company. They are under the influence of the volatility of financial markets. Technology here can help by analyzing different scenarios but the ultimate decision is in the hands of the banks. 

Challenges & opportunities in adoption of AI

Why Insurers hesitate to invest in AI?

Mr. Horacio:  Companies were investing in technology earlier, but now it has accelerated due to the unprecedented change brought about by COVID-19 pandemic. Not just the developed countries but in developing countries such as Latin America, I see a big wave of new investments in technology. Technology companies are also looking forward to this change. Insurers will eventually overcome their hesitation and invest more in AI and other technologies. 

[Related: 5 Challenges in AI implementation for Insurers]

Which area will see max Investment in AI- claims, underwriting, fraud detection, marketing in Argentina, and Latin American Insurance markets?

Mr. Horacio: Before the outbreak of the COVID-19 pandemic, investment in AI was more targeted towards claims, fraud, underwriting, back-office operations, etc. Going forward, predicting future scenarios will be a challenge. Historical data might not be useful here. Therefore, in the New Normal, all aspects of an Insurance company will have to be developed under the umbrella of AI.

Product Innovation

Consumers, now more than ever are seeking value-added experiences with the products & services they buy. How will these expectations amidst this Pandemic backdrop impact new product innovation within insurance? 

Mr. Horacio: Customers want a more palpable relationship with their insurers. Customer Experience is going to be a fundamental aspect during the purchase of insurance coverage. In addition to being simple, the purchase and subscription process will have to be perceived as a service that accompanies the client at every moment they need it. These additions will help insurers gain more information on their customer’s actions and behavior. Based on this data, they can dynamically adapt the coverage and pricing of the product. I call it — Dynamic-on-demand coverage. 

Challenges in Latin American Insurance industry

What are some of the technological challenges faced by Insurers in Argentina and Latin American markets operating in the New Normal? 

Mr. Horacio: Insurance industry for many years has been static but now is moving forward in many ways. The world including the Latin American Insurance is witnessing rapid development in terms of technology. The InsurTech industry is parallel to the Insurance industry. It aids in the development of the insurance companies. The whole world of insurance is making advances in technology. Different economics have different buying patterns for insurance products. One such insurance product that should develop is Microinsurance

Microinsurance needs technology, without which it is very difficult to manage. In a sense, the outbreak of COVID-19 was beneficial in accelerating these technological developments.   

[Related – AI can help bridge customer gaps for microinsurers]

Insurance buying behavior in the post-pandemic world

In a post-pandemic World, will insurance ever be bought offline? Or have we crossed the threshold for now buying policies purely online? 

Mr. Horacio:  Personal line insurances such as car, accident, personal, travel, microinsurance are mostly purchased online. In Argentina, 60% of the insurance policies are sold by traditional marketing and sales through brokers. However, in commercial, industrial, energy, transport, and large companies in general, the marketing and sales will continue the traditional ways but through electronic means. The use of IoT, sensors, drones dynamically monitoring the facilities and processes in different industries is increasing. Argentina, which is an agro-based country already has technologies such as drones and IoT which monitor the crops in place. AI will surely be crucial here to analyze the data and enable quick decisions in case of a fire or an accident.  

Wrapping up

Summing up — Mr. Horacio Sanchez-Granel shared valuable insights on the challenges in the Latin American Insurance Industry, how AI technologies can aid in policymaking and rise in dynamic-on-demand policies in the post-pandemic world.

AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

Podcasts in this series:

Cancel

Knowledge thats worth delivered in your inbox

AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

Image Source

  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot